We used a network similar to the 3D U-Net [1]}, which we modified by adding residual connections with short and long skip connections. We set the number of features in the first stage of the encoder part of the network to 14, which was the largest possible on our GPU memory. The model worked on \(96\times 96\times 96\) -voxel image blocks. During training, we sampled blocks from random locations in the training images. Other data augmentation methods used during training included flip, rotation by integer multiples of \(\pi /2\) , and addition of random Gaussian noise to voxel intensity values. We also experimented with elastic deformation for data augmentation ([2]}, [3]}), but we did not pursue that augmentation method because it negatively impacted segmentation of fine structures such as those in the brain cortical plate. On a test image, a sliding window approach with a 24-voxel overlap between adjacent blocks was used to process the image. We used the negative of the Dice Similarity Coefficient (DSC) between the predicted and target probability maps as the loss function and Adam [4]} as the optimization method. We used an initial learning rate of \(10^{-4}\) , which was reduced by 0.90 after every 2000 training iterations if the loss did not decrease. If the loss did not decrease for two consecutive evaluations, we stopped the training and claimed convergence. This typically occurred after 100-150 training epochs through all training images.
